2 citations · 5 across the 6 of their papers we have counts for
4 papers · 1 filter
NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database
Weizhi Fei, Hao Shi, Jing Xu +7
Efficiently editing knowledge stored in large language models (LLMs) enables model updates without large-scale training. One possible solution is Locate-and-Edit (L\&E), allowing s…
Efficient Prompt Compression with Evaluator Heads for Long-Context Transformer Inference
Weizhi Fei, Xueyan Niu, Guoqing Xie +3
Although applications involving long-context inputs are crucial for the effective utilization of large language models (LLMs), they also result in increased computational costs and…
Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding
Weizhi Fei, Xueyan Niu, Guoqing Xie +4
Current Large Language Models (LLMs) face inherent limitations due to their pre-defined context lengths, which impede their capacity for multi-hop reasoning within extensive textua…
Extending Context Window of Large Language Models via Semantic Compression
Weizhi Fei, Xueyan Niu, Pingyi Zhou +4
Transformer-based Large Language Models (LLMs) often impose limitations on the length of the text input to ensure the generation of fluent and relevant responses. This constraint r…